Call for Papers
The 15th Asian Conference on Machine Learning (ACML 2023) will take place between November 11 - 14, 2023 in İstanbul, Turkey. The conference aims to provide a leading international forum for researchers in machine learning and related fields to share their new ideas, progress and achievements.
The conference calls for high-quality, original research papers in the theory and practice of machine learning. The conference also solicits proposals focusing on frontier research, new ideas and paradigms in machine learning. We encourage submissions from all parts of the world, not only confined to the Asia-Pacific region.
The conference runs two publication tracks, authors may submit either to:
- Conference Track: (16-page limit with references) for which the proceedings will be published as a volume of Proceedings of Machine Learning Research Workshop and Conference Proceedings (PMLR).
- Journal Track: (20-page limit with references) for which accepted papers will appear in a special issue of the Springer Machine Learning Journal (MLJ).
Important Dates
Kindly note that all deadlines would be at 23:59 AoE (Anywhere on Earth).
(subject to minor changes in case there are conflicts with timelines of other major ML conferences)
Conference Track | |
| Submission deadline | |
| Reviews released to authors | |
| Author rebuttal deadline | |
| 08 September 2023 | Acceptance notification |
| 29 September 2023 | Camera-ready submission deadline |
Journal Track | |
| Submission deadline | |
| 1st round review results (accept, minor revision, or reject) | |
| Revised manuscript submission deadline (for minor revision papers) | |
| Acceptance notification | |
| 29 September 2023 | Camera-ready submission deadline |
Topics
Topics of interest include but are not limited to:
- General machine learning methodologies
- Active learning
- Bayesian machine learning
- Dimensionality reduction
- Feature selection
- Graphical models
- Imitation Learning
- Latent variable models
- Learning for big data
- Learning from noisy supervision
- Learning in graphs
- Multi-objective learning
- Multiple instance learning
- Multi-task learning
- Neuro-symbolic methods
- Online learning
- Optimization
- Reinforcement learning
- Relational learning
- Semi-supervised learning
- Sparse learning
- Structured output learning
- Supervised learning
- Transfer learning
- Unsupervised learning
- Other machine learning methodologies
- Deep learning
- Architectures
- Attention mechanism and transformers
- Deep learning theory
- Deep reinforcement learning
- Generative models
- Supervised learning
- Other topics in deep learning
- Theory
- Bandits
- Computational learning theory
- Game theory
- Matrix/tensor methods
- Optimization
- Statistical learning theory
- Other theories
- Datasets and reproducibility
- Implementations, libraries
- ML datasets and benchmarks
- Other topics in reproducible ML research
- Trustworthy machine learning
- Accountability, explainability, transparency
- Causality
- Fairness
- Privacy
- Robustness
- Other topics in trustworthy ML
- Learning in knowledge-intensive systems
- Knowledge refinement and theory revision
- Multi-strategy learning
- Other systems
- Applications
- Bioinformatics
- Biomedical informatics
- Climate science
- Collaborative filtering
- Computer vision
- COVID-19 related research
- Healthcare
- Human activity recognition
- Information retrieval
- Natural language processing
- Social good
- Social networks
- Web search
- Other applications
Submission Instructions
Similar to previous years, ACML 2023 has two publication tracks. Authors may submit either to:
- Conference Track: (16-page limit with references) for which the proceedings will be published as a volume of Proceedings of Machine Learning Research Workshop and Conference Proceedings (PMLR).
- Journal Track: (20-page limit with references) for which accepted papers will appear in a special issue of the Springer Machine Learning Journal (MLJ).
Please note that submission procedures for the two tracks are different. Please read the instructions carefully before submitting.
Conference Track
Submission Deadline:For the conference track, please submit your manuscript via CMT at: https://cmt3.research.microsoft.com/ACML2023.
IMPORTANT: When creating a new submission on CMT, please ensure you choose the "Conference" track.
The Latex submission template and style file can be found here: ACML2023_submission_template.zip.
The Latex camera-ready template and style file can be found here: ACML2023_camera-ready_template.zip.
Manuscripts must be written in English, be a maximum of 16 pages (including references, appendices, etc.) and follow the PMLR style. If required, supplementary material may be submitted as a separate file, but reviewers are not obliged to consider this.
All conference track submissions must be anonymized. Submissions that are not anonymized, over-length, or not in the correct format will be rejected without review. To anonymize, simply leave the author information empty in the Tex template. There is no separate format for anonymizing.
It is not appropriate to submit papers that are substantially similar to versions that have been previously published, or accepted for publication, or that have been submitted in parallel to other conferences or journals. However, submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published. Also, submission is permitted for papers that are available as a technical report (e.g., in arXiv) as long as it is not cited in the submission.
Journal Track
Submission Deadline:In addition to the conference track, this year’s ACML will run a journal track, similar to previous years. Papers that are accepted to the journal track must be presented at the conference in order to be published.
IMPORTANT: Similar to previous years, for the journal track, the abstract and the paper must be submitted to two different systems simultaneously for the purpose of review management:
- First, please submit ONLY the title and abstract via CMT at: https://cmt3.research.microsoft.com/ACML2023. (paper manuscript need not be submitted here). When creating the submission on CMT, please choose the "Journal" track.
- Then, please submit the paper via Springer’s Editorial Manager system at: https://www.editorialmanager.com/mach. When creating a new submission on Springer’s Editorial Manager, please make sure to choose "S.I.: ACML 2023" as the article type.
For the journal track, manuscripts must be written in English with a maximum of 20 pages (including references, appendices, etc.). For the template and style files, please follow the instructions for authors on the journal website: https://www.springer.com/computer/ai/journal/10994.
The journal track will follow the reviewing process of the Machine Learning journal. This includes allowing papers that require minor changes to be resubmitted after a first-round review. The journal track committee will aim to complete the reviewing process in time for this year’s conference. In the unlikely event that the reviewing process for a paper is not completed in time (for this year’s conference), the paper will not be considered for the conference and the review will be completed as a regular submission to the Machine Learning journal.
The journal track review is single-blind, i.e., the authors’ identity will be visible to reviewers. It is not appropriate to submit papers that are substantially similar to versions that have been previously published, or accepted for publication, or that have been submitted in parallel to other conferences or journals. Submissions that are not in the correct format will be rejected without review. In addition, extended versions of published conference papers are not eligible for journal track submission. However, submission is permitted for papers presented or to be presented at conferences or workshops without proceedings, or with only abstracts published. Also, submission is permitted for papers that are available as a technical report (e.g., in arXiv).